Evolutionary Algorithms

Evolutionary algorithms (EAs) are population-based metaheuristics, originally inspired by aspects of natural evolution. Modern varieties incorporate a broad mixture of search mechanisms, and tend to blend inspiration from nature with pragmatic engineering concerns; however, all EAs essentially operate by maintaining a population of potential solutions and in some way artificially 'evolving' that population over time. Particularly well-known categories of EAs include genetic algorithms (GAs), Genetic Programming (GP), and Evolution Strategies (ES). EAs have proven very successful in practical applications, particularly those requiring solutions to combinatorial problems. EAs are highly flexible and can be configured to address any optimization task, without the requirements for reformulation and/or simplification that would be needed for other techniques. However, this flexibility goes hand in hand with a cost: the tailoring of an EA's configuration and parameters, so as to provide robust performance for a given class of tasks, is often a complex and time-consuming process. This tailoring process is one of the many ongoing research areas associated with EAs.Comment: To appear in R. Marti, P. Pardalos, and M. Resende, eds., Handbook of Heuristics, Springe

Using Epigenetic Networks for the Analysis of Movement Associated with Levodopa Therapy for Parkinson's Disease

© 2016 The Author(s) Levodopa is a drug that is commonly used to treat movement disorders associated with Parkinson's disease. Its dosage requires careful monitoring, since the required amount changes over time, and excess dosage can lead to muscle spasms known as levodopa-induced dyskinesia. In this work, we investigate the potential for using epiNet, a novel artificial gene regulatory network, as a classifier for monitoring accelerometry time series data collected from patients undergoing levodopa therapy. We also consider how dynamical analysis of epiNet classifiers and their transitions between different states can highlight clinically useful information which is not available through more conventional data mining techniques. The results show that epiNet is capable of discriminating between different movement patterns which are indicative of either insufficient or excessive levodopa

A Centrality Based Multi-Objective Disease-Gene Association Approach Using Genetic Algorithms

The Disease Gene Association Problem (DGAP) is a bioinformatics problem in which genes are ranked with respect to how involved they are in the presentation of a particular disease. Previous approaches have shown the strength of both Monte Carlo and evolutionary computation (EC) based techniques. Typically these past approaches improve ranking measures, develop new gene relation definitions, or implement more complex EC systems. This thesis presents a hybrid approach which implements a multi-objective genetic algorithm, where input consists of centrality measures based on various relational biological evidence types merged into a complex network. In an effort to explore the effectiveness of the technique compared to past work, multiple objective settings and different EC parameters are studied including the development of a new exchange methodology, safe dealer-based (SDB) crossover. Successful results with respect to breast cancer and Parkinson's disease compared to previous EC techniques and popular known databases are shown. In addition, the newly developed methodology is also successfully applied to Alzheimer’s, further demonstrating the flexibility of the technique. Across all three cases studies the strongest results were produced by the shortest path-based measures stress and betweenness in a single objective parameter setting. When used in conjunction in a multi-objective environment, competitive results were also obtained but fell short of the single objective settings studied as part of this work. Lastly, while SDB crossover fell short of expectations on breast cancer and Parkinson's, it achieved the best results when applied to Alzheimer’s, illustrating the potential of the technique for future study

An Evaluation of Performance Enhancements to Particle Swarm Optimisation on Real-World Data

Swarm Computation is a relatively new optimisation paradigm. The basic premise is to model the collective behaviour of self-organised natural phenomena such as swarms, flocks and shoals, in order to solve optimisation problems. Particle Swarm Optimisation (PSO) is a type of swarm computation inspired by bird flocks or swarms of bees by modelling their collective social influence as they search for optimal solutions. In many real-world applications of PSO, the algorithm is used as a data pre-processor for a neural network or similar post processing system, and is often extensively modified to suit the application. The thesis introduces techniques that allow unmodified PSO to be applied successfully to a range of problems, specifically three extensions to the basic PSO algorithm: solving optimisation problems by training a hyperspatial matrix, using a hierarchy of swarms to coordinate optimisation on several data sets simultaneously, and dynamic neighbourhood selection in swarms. Rather than working directly with candidate solutions to an optimisation problem, the PSO algorithm is adapted to train a matrix of weights, to produce a solution to the problem from the inputs. The search space is abstracted from the problem data. A single PSO swarm optimises a single data set and has difficulties where the data set comprises disjoint parts (such as time series data for different days). To address this problem, we introduce a hierarchy of swarms, where each child swarm optimises one section of the data set whose gbest particle is a member of the swarm above in the hierarchy. The parent swarm(s) coordinate their children and encourage more exploration of the solution space. We show that hierarchical swarms of this type perform better than single swarm PSO optimisers on the disjoint data sets used. PSO relies on interaction between particles within a neighbourhood to find good solutions. In many PSO variants, possible interactions are arbitrary and fixed on initialisation. Our third contribution is a dynamic neighbourhood selection: particles can modify their neighbourhood, based on the success of the candidate neighbour particle. As PSO is intended to reflect the social interaction of agents, this change significantly increases the ability of the swarm to find optimal solutions. Applied to real-world medical and cosmological data, this modification is and shows improvements over standard PSO approaches with fixed neighbourhoods

EMOCS: evolutionary multi-objective optimisation for clinical scorecard generation

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordClinical scorecards of risk factors associated with disease severity or mortality outcome are used by clinicians to make treatment decisions and optimize resources. This study develops an automated tool or framework based on evolutionary algorithms for the derivation of scorecards from clinical data. The techniques employed are based on the NSGA-II Multi-objective Optimization Genetic Algorithm (GA) which optimizes the Pareto-front of two clinically-relevant scorecard objectives, size and accuracy. Three automated methods are presented which improve on previous manually derived scorecards. The first is a hybrid algorithm which uses the GA for feature selection and a decision tree for scorecard generation. In the second, the GA generates the full scorecard. The third is an extended full scoring system in which the GA also generates the scorecard scores. In this system combinations of features and thresholds for each scorecard point are selected by the algorithm and the evolutionary process is used to discover near-optimal Pareto-fronts of scorecards for exploration by expert decision makers. This is shown to produce scorecards that improve upon a human derived example for C.Difficile, an important infection found globally in communities and hospitals, although the methods described are applicable to any disease where the required data is available.Engineering and Physical Sciences Research Council (EPSRC

An improved data classification framework based on fractional particle swarm optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications

The chemical and computational biology of inflammation

Non-communicable diseases (NCD) such as cancer, heart disease and cerebrovascular injury are dependent on or aggravated by inflammation. Their prevention and treatment is arguably one of the greatest challenges to medicine in the 21st century. The pleiotropic, proinflammatory cytokine; interleukin-l beta (IL-l~) is a primary, causative messenger of inflammation. Lipopolysaccharide (LPS) induction ofIL-l~ expression via toll-like receptor 4 (TLR4) in myeloid cells is a robust experimental model of inflammation and is driven in large part via p38-MAPK and NF-KB signaling networks. The control of signaling networks involved in IL-l~ expression is distributed and highly complex, so to perturb intracellular networks effectively it is often necessary to modulate several steps simultaneously. However, the number of possible permutations for intervention leads to a combinatorial explosion in the experiments that would have to be performed in a complete analysis. We used a multi-objective evolutionary algorithm (EA) to optimise reagent combinations from a dynamic chemical library of 33 compounds with established or predicted targets in the regulatory network controlling IL-l ~ expression. The EA converged on excellent solutions within 11 generations during which we studied just 550 combinations out of the potential search space of - 9 billion. The top five reagents with the greatest contribution to combinatorial effects throughout the EA were then optimised pair- wise with respect to their concentrations, using an adaptive, dose matrix search protocol. A p38a MAPK inhibitor (30 ± 10% inhibition alone) with either an inhibitor of IKB kinase (12 ± 9 % inhibition alone) or a chelator of poorly liganded iron (19 ± 8 % inhibition alone) yielded synergistic inhibition (59 ± 5 % and 59 ± 4 % respectively, n=7, p≥O.04 for both combinations, tested by one way ANOVA with Tukey's multiple test correction) of macrophage IL-l~ expression. Utilising the above data, in conjunction with the literature, an LPS-directed transcriptional map of IL-l ~ expression was constructed. Transcription factors (TF) targeted by the signaling networks coalesce at precise nucleotide binding elements within the IL-l~ regulatory DNA. Constitutive binding of PU.l and C/EBr-~ TF's are obligate for IL-l~ expression. The findings in this thesis suggest that PU.l and C/EBP-~ TF's form scaffolds facilitating dynamic control exerted by other TF's, as exemplified by c-Jun. Similarly, evidence is emerging that epigenetic factors, such as the hetero-euchromatin balance, are also important in the relative transcriptional efficacy in different cell types. Evolutionary searches provide a powerful and general approach to the discovery of novel combinations of pharmacological agents with potentially greater therapeutic indices than those of single drugs. Similarly, construction of signaling network maps aid the elucidation of pharmacological mechanism and are mandatory precursors to the development of dynamic models. The symbiosis of both approaches has provided further insight into the mechanisms responsible for IL-lβ expression, and reported here provide a - platform for further developments in understanding NCD's dependent on or aggravated by inflammation.EThOS - Electronic Theses Online ServiceBBSRCEPSRCGBUnited Kingdo

VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

Rethinking drug design in the artificial intelligence era

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them